Many on-line, handwriting recognition systems employ curve matching methods to match an unknown character against prototype, or template, characters. One example is T. Fujisaki, T. F. Chefalas, J. Kim and C. C. Tappert, "Online recognizer for runon handprinted characters," Proc. 10th Int. Conf. Pattern Recognition, pp. 450-454, June 1990. In general, the recognition accuracy of such prototype-based handwriting recognition systems is a function of the quality of the prototypes, which in turn, is dependent upon the efficiency and accuracy of system training.
Occasionally, errors are made by the recognition system and, when they occur, the user corrects them. However, in current systems there is no provision for training during error correction to prevent future similar errors. The training that is available requires the user to enter a special training mode. This mode switching interrupts the normal work flow of the user.
The following U.S. and Japanese Patents all teach various aspects of handwriting recognition systems.
U.S. Pat. No. 4,561,105, issued Dec. 24, 1985, entitled "Complex Pattern Recognition Method and System" to Crane et al describes an on-line character recognition system that uses template or model strokes (col 3, lines 1-52).
U.S. Pat. No, 5,285,505, issued Feb. 8, 1994, entitled "Method and Apparatus for Improving Prototypes of Similar Characters in On-Line Recognition" to Kim et al, describes a character recognition system for improving the prototypes of similar characters by emphasizing the dissimilarities of prototypes of similar characters and deemphasizing the similarities.
Japanese Patent No. 62-24382 entitled "Method for Recognizing Handwritten Character" describes, in the abstract, a method for recognizing handwritten characters in which input strokes are compared with reference strokes stored in a template memory.
C. Chiang and H. Fu, "Using Neural Nets to Recognize Handwritten/Printed Characters", IEEE Proc. Advanced Computer Technology, Reliable Systems and Applications, pp. 492-96, 1991, describes a handwritten character recognition system implemented by a stochastical neural network which can provide on-line training of a handwriting of the system.
What is not taught by the foregoing references, and what is thus an object of the invention to provide, is a method and apparatus for improving or optimizing a set of character prototypes, by training the system to recognize a particular user's style of writing.
A further object of the invention is to provide a method, and a system for accomplishing the method, for silently training (i.e., training performed without explicit knowledge of, or direction by, the user) the system to recognize a particular user's handwriting through user feedback, and without the user having to specifically request training.
Another object of the invention is to provide an efficient method of using the information provided during error correction to modify the prototypes so as to reduce the likelihood of similar errors occurring in the future. Such a method permits training to occur over time (that is, the prototype set to evolve) as a natural process without having the user switch modes.
The foregoing and other problems are overcome and the objects of the invention are realized by a method for silent training by error correction for an on-line handwriting recognition system.